We follow the design of the semantic segmentation network DeepLabV3 [1]} to estimate the categorical depth distributions from image features \(\mathbf {\tilde{F}}\)  (Depth Distribution Network in Figure REF ), where we modify the network to produce pixel-wise probability scores of belonging to depth bins rather than semantic classes with a downsample-upsample architecture. Image features \(\mathbf {\tilde{F}}\)  are downsampled with the remaining components of the ResNet-101 [2]} backbone (Block2, Block3, and Block4). An atrous spatial pyramid pooling [1]} (ASPP) module is applied to capture multi-scale information, where the number of output channels is set as \(D\) . The output of the ASPP module is upsampled to the original feature size with bilinear interpolation to produce the categorical depth distributions \(\mathbf {D} \in \mathbb {R}^{W_F \times H_F \times D}\) . A softmax function is applied for each pixel to normalize the \(D\)  logits into probabilities between 0 and 1.
